<![CDATA[The classification of disease diagnoses using the International Classification of Diseases (ICD-10) standard is essential for supporting clinical decision-making and administrative processes in healthcare systems. This study evaluated the performance of three machine learning algorithms, namely decision tree, random forest, and support vector machine (SVM), for ICD-10 diagnosis classification using 3,730 textual medical record entries collected from the Klinik Pratama UIN Sunan Kalijaga, Yogyakarta, Indonesia. The dataset exhibited significant class imbalance, which was addressed using the synthetic minority oversampling technique (SMOTE). The preprocessing procedures included text normalization and Term frequency-inverse document frequency (TF-IDF) vectorization, followed by model development with hyperparameter tuning through grid search cross validation. Model performance was assessed using accuracy, precision, recall, F1-score, confusion matrix, and five-fold cross validation. Random forest achieved the highest mean accuracy at 93.65%, followed by decision tree at 92.25% and SVM at 87.91%. These results indicate that ensemble-based approaches provide more reliable classification outcomes for imbalanced textual medical data. The findings are expected to support the development of semi-automated ICD-10 coding systems and improve the efficiency and accuracy of medical coding workflows.]]>
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